Pushing the docs to dev/ for branch: main, commit 485d39cb691d1c7a231a237f186b87f451387799

Author: sklearn-ci

Diff for: dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Caching nearest neighbors\n\nThis examples demonstrates how to precompute the k nearest neighbors before\nusing them in KNeighborsClassifier. KNeighborsClassifier can compute the\nnearest neighbors internally, but precomputing them can have several benefits,\nsuch as finer parameter control, caching for multiple use, or custom\nimplementations.\n\nHere we use the caching property of pipelines to cache the nearest neighbors\ngraph between multiple fits of KNeighborsClassifier. The first call is slow\nsince it computes the neighbors graph, while subsequent call are faster as they\ndo not need to recompute the graph. Here the durations are small since the\ndataset is small, but the gain can be more substantial when the dataset grows\nlarger, or when the grid of parameter to search is large.\n"
+        "\n# Caching nearest neighbors\n\nThis example demonstrates how to precompute the k nearest neighbors before\nusing them in KNeighborsClassifier. KNeighborsClassifier can compute the\nnearest neighbors internally, but precomputing them can have several benefits,\nsuch as finer parameter control, caching for multiple use, or custom\nimplementations.\n\nHere we use the caching property of pipelines to cache the nearest neighbors\ngraph between multiple fits of KNeighborsClassifier. The first call is slow\nsince it computes the neighbors graph, while subsequent calls are faster as they\ndo not need to recompute the graph. Here the durations are small since the\ndataset is small, but the gain can be more substantial when the dataset grows\nlarger, or when the grid of parameter to search is large.\n"
       ]
     },
     {

Diff for: dev/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip

@@ -7,10 +7,6 @@
 matrix decomposition (dimension reduction) methods from the module
 :mod:`sklearn.decomposition` (see the documentation chapter
 :ref:`decompositions`).
-
-
-- Authors: Vlad Niculae, Alexandre Gramfort
-- License: BSD 3 clause
 """
 
 # Authors: The scikit-learn developers

Diff for: dev/_downloads/038d1885eb5f5ea53aca42da3031fe38/plot_document_clustering.zip

@@ -11,12 +11,6 @@
 
 We use the Diabetes dataset, which consists of 10 features collected from 442
 diabetes patients.
-
-Authors: `Manoj Kumar <[email protected]>`_,
-`Maria Telenczuk <https://github.com/maikia>`_, Nicolas Hug.
-
-License: BSD 3 clause
-
 """
 
 # Authors: The scikit-learn developers

Diff for: dev/_downloads/03c018a16384c69f3a89e473650d57ee/plot_svm_margin.zip

@@ -3,15 +3,15 @@
 Caching nearest neighbors
 =========================
 
-This examples demonstrates how to precompute the k nearest neighbors before
+This example demonstrates how to precompute the k nearest neighbors before
 using them in KNeighborsClassifier. KNeighborsClassifier can compute the
 nearest neighbors internally, but precomputing them can have several benefits,
 such as finer parameter control, caching for multiple use, or custom
 implementations.
 
 Here we use the caching property of pipelines to cache the nearest neighbors
 graph between multiple fits of KNeighborsClassifier. The first call is slow
-since it computes the neighbors graph, while subsequent call are faster as they
+since it computes the neighbors graph, while subsequent calls are faster as they
 do not need to recompute the graph. Here the durations are small since the
 dataset is small, but the gain can be more substantial when the dataset grows
 larger, or when the grid of parameter to search is large.

Diff for: dev/_downloads/04b9a7769df5b331f4d94e1d065b4311/plot_voting_decision_regions.zip

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Early stopping in Gradient Boosting\n\nGradient Boosting is an ensemble technique that combines multiple weak\nlearners, typically decision trees, to create a robust and powerful\npredictive model. It does so in an iterative fashion, where each new stage\n(tree) corrects the errors of the previous ones.\n\nEarly stopping is a technique in Gradient Boosting that allows us to find\nthe optimal number of iterations required to build a model that generalizes\nwell to unseen data and avoids overfitting. The concept is simple: we set\naside a portion of our dataset as a validation set (specified using\n`validation_fraction`) to assess the model's performance during training.\nAs the model is iteratively built with additional stages (trees), its\nperformance on the validation set is monitored as a function of the\nnumber of steps.\n\nEarly stopping becomes effective when the model's performance on the\nvalidation set plateaus or worsens (within deviations specified by `tol`)\nover a certain number of consecutive stages (specified by `n_iter_no_change`).\nThis signals that the model has reached a point where further iterations may\nlead to overfitting, and it's time to stop training.\n\nThe number of estimators (trees) in the final model, when early stopping is\napplied, can be accessed using the `n_estimators_` attribute. Overall, early\nstopping is a valuable tool to strike a balance between model performance and\nefficiency in gradient boosting.\n\nLicense: BSD 3 clause\n"
+        "\n# Early stopping in Gradient Boosting\n\nGradient Boosting is an ensemble technique that combines multiple weak\nlearners, typically decision trees, to create a robust and powerful\npredictive model. It does so in an iterative fashion, where each new stage\n(tree) corrects the errors of the previous ones.\n\nEarly stopping is a technique in Gradient Boosting that allows us to find\nthe optimal number of iterations required to build a model that generalizes\nwell to unseen data and avoids overfitting. The concept is simple: we set\naside a portion of our dataset as a validation set (specified using\n`validation_fraction`) to assess the model's performance during training.\nAs the model is iteratively built with additional stages (trees), its\nperformance on the validation set is monitored as a function of the\nnumber of steps.\n\nEarly stopping becomes effective when the model's performance on the\nvalidation set plateaus or worsens (within deviations specified by `tol`)\nover a certain number of consecutive stages (specified by `n_iter_no_change`).\nThis signals that the model has reached a point where further iterations may\nlead to overfitting, and it's time to stop training.\n\nThe number of estimators (trees) in the final model, when early stopping is\napplied, can be accessed using the `n_estimators_` attribute. Overall, early\nstopping is a valuable tool to strike a balance between model performance and\nefficiency in gradient boosting.\n"
       ]
     },
     {

Diff for: dev/_downloads/053b58bbfc8177072856c743b2c93424/plot_varimax_fa.zip

@@ -27,9 +27,6 @@
 applied, can be accessed using the `n_estimators_` attribute. Overall, early
 stopping is a valuable tool to strike a balance between model performance and
 efficiency in gradient boosting.
-
-License: BSD 3 clause
-
 """
 
 # Authors: The scikit-learn developers

Diff for: dev/_downloads/06c18f4675ecd124f98537d05e10abd0/plot_nca_dim_reduction.zip

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Model-based and sequential feature selection\n\nThis example illustrates and compares two approaches for feature selection:\n:class:`~sklearn.feature_selection.SelectFromModel` which is based on feature\nimportance, and\n:class:`~sklearn.feature_selection.SequentialFeatureSelector` which relies\non a greedy approach.\n\nWe use the Diabetes dataset, which consists of 10 features collected from 442\ndiabetes patients.\n\nAuthors: [Manoj Kumar]([email protected]),\n[Maria Telenczuk](https://github.com/maikia), Nicolas Hug.\n\nLicense: BSD 3 clause\n"
+        "\n# Model-based and sequential feature selection\n\nThis example illustrates and compares two approaches for feature selection:\n:class:`~sklearn.feature_selection.SelectFromModel` which is based on feature\nimportance, and\n:class:`~sklearn.feature_selection.SequentialFeatureSelector` which relies\non a greedy approach.\n\nWe use the Diabetes dataset, which consists of 10 features collected from 442\ndiabetes patients.\n"
       ]
     },
     {

Diff for: dev/_downloads/06e5cfec52777b1f220312b4ada9469d/plot_document_classification_20newsgroups.zip

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Faces dataset decompositions\n\nThis example applies to `olivetti_faces_dataset` different unsupervised\nmatrix decomposition (dimension reduction) methods from the module\n:mod:`sklearn.decomposition` (see the documentation chapter\n`decompositions`).\n\n\n- Authors: Vlad Niculae, Alexandre Gramfort\n- License: BSD 3 clause\n"
+        "\n# Faces dataset decompositions\n\nThis example applies to `olivetti_faces_dataset` different unsupervised\nmatrix decomposition (dimension reduction) methods from the module\n:mod:`sklearn.decomposition` (see the documentation chapter\n`decompositions`).\n"
       ]
     },
     {